Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

Abstract

Distributed edge computing technology for artificial intelligence refers to an emerging technology that integrates network core processing functions, computing functions, storage functions, etc. into one end source closer to objects or data on the basis of an open platform to optimize service quality. In this paper, distributed edge computing technology is applied to footprint extraction and sports dance action recognition, aimed at improving the recognition efficiency and recognition quality. Firstly, the overview of edge computing theory is introduced; these include edge computing concepts, edge computing characteristics, and edge computing platforms; and then, the classification of action recognition technology is described. Finally, the edge computing recognition technology and traditional recognition technology are compared and tested. The experimental results show that the average accuracy of edge computing technology for footprint extraction can reach 98.98, and the average recognition rate of sports dance movements can reach 80.21%, which verifies its practicability.